Overview

Dataset statistics

Number of variables19
Number of observations100514
Missing cells105653
Missing cells (%)5.5%
Duplicate rows10216
Duplicate rows (%)10.2%
Total size in memory14.6 MiB
Average record size in memory152.0 B

Variable types

Categorical7
Numeric12

Alerts

Dataset has 10216 (10.2%) duplicate rowsDuplicates
Loan ID has a high cardinality: 81999 distinct valuesHigh cardinality
Customer ID has a high cardinality: 81999 distinct valuesHigh cardinality
Annual Income is highly overall correlated with Monthly DebtHigh correlation
Monthly Debt is highly overall correlated with Annual Income and 1 other fieldsHigh correlation
Number of Credit Problems is highly overall correlated with BankruptciesHigh correlation
Current Credit Balance is highly overall correlated with Monthly Debt and 1 other fieldsHigh correlation
Maximum Open Credit is highly overall correlated with Current Credit BalanceHigh correlation
Bankruptcies is highly overall correlated with Number of Credit ProblemsHigh correlation
Purpose is highly imbalanced (66.1%)Imbalance
Credit Score has 19668 (19.6%) missing valuesMissing
Annual Income has 19668 (19.6%) missing valuesMissing
Years in current job has 4736 (4.7%) missing valuesMissing
Months since last delinquent has 53655 (53.4%) missing valuesMissing
Annual Income is highly skewed (γ1 = 46.88869873)Skewed
Maximum Open Credit is highly skewed (γ1 = 132.6388749)Skewed
Loan ID is uniformly distributedUniform
Customer ID is uniformly distributedUniform
Number of Credit Problems has 86035 (85.6%) zerosZeros
Bankruptcies has 88774 (88.3%) zerosZeros
Tax Liens has 98062 (97.6%) zerosZeros

Reproduction

Analysis started2023-08-29 23:41:16.963545
Analysis finished2023-08-29 23:42:21.677248
Duration1 minute and 4.71 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Loan ID
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct81999
Distinct (%)82.0%
Missing514
Missing (%)0.5%
Memory size785.4 KiB
ede97bab-caf2-4b16-9353-efc166df4c50
 
2
c5affebf-2290-454b-8514-82923b120bce
 
2
1e5509c6-96b2-4f80-930a-4afa804a7672
 
2
2e6c0462-5c05-424b-b31b-6588cbca7d52
 
2
427ba12e-9f21-4e69-affe-7f1d95cb1587
 
2
Other values (81994)
99990 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters3600000
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63998 ?
Unique (%)64.0%

Sample

1st row14dd8831-6af5-400b-83ec-68e61888a048
2nd row4771cc26-131a-45db-b5aa-537ea4ba5342
3rd row4eed4e6a-aa2f-4c91-8651-ce984ee8fb26
4th row77598f7b-32e7-4e3b-a6e5-06ba0d98fe8a
5th rowd4062e70-befa-4995-8643-a0de73938182

Common Values

ValueCountFrequency (%)
ede97bab-caf2-4b16-9353-efc166df4c50 2
 
< 0.1%
c5affebf-2290-454b-8514-82923b120bce 2
 
< 0.1%
1e5509c6-96b2-4f80-930a-4afa804a7672 2
 
< 0.1%
2e6c0462-5c05-424b-b31b-6588cbca7d52 2
 
< 0.1%
427ba12e-9f21-4e69-affe-7f1d95cb1587 2
 
< 0.1%
c2e22a73-7668-4c93-8d18-2f1fe6348c27 2
 
< 0.1%
afee91ac-2aa0-4731-aad1-bd07e28d6943 2
 
< 0.1%
e9f492e7-0122-4523-bb85-eca6658fa5cf 2
 
< 0.1%
752d658a-55d2-431d-9953-d9f275e87e9f 2
 
< 0.1%
cf24dd94-3966-4cb9-a723-7e16559ab73c 2
 
< 0.1%
Other values (81989) 99980
99.5%
(Missing) 514
 
0.5%

Length

2023-08-29T23:42:21.869159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ede97bab-caf2-4b16-9353-efc166df4c50 2
 
< 0.1%
e7d6cc4c-012c-4221-887d-89f7ec2c32af 2
 
< 0.1%
13e52871-68a6-4cf1-8f0a-13a4c6cc44a3 2
 
< 0.1%
d75e7609-0432-451e-9d6f-eb064d94db44 2
 
< 0.1%
2d26a0f0-cf0d-45f2-85c0-fb43e7c81fd6 2
 
< 0.1%
4b73ff1d-1585-4936-b60a-ba86527fd69c 2
 
< 0.1%
67a34840-e0d3-41a1-b94e-395600300989 2
 
< 0.1%
cfa6ef12-53b2-40c2-a213-1baf3ec664a7 2
 
< 0.1%
b17d4708-8f51-4297-989f-e1c11563919d 2
 
< 0.1%
4691d4a1-993b-4163-94a6-1dfff439b0ab 2
 
< 0.1%
Other values (81989) 99980
> 99.9%

Most occurring characters

ValueCountFrequency (%)
- 400000
 
11.1%
4 287551
 
8.0%
a 212644
 
5.9%
9 212346
 
5.9%
b 211946
 
5.9%
8 211864
 
5.9%
0 188172
 
5.2%
2 188148
 
5.2%
c 188078
 
5.2%
7 187813
 
5.2%
Other values (7) 1311438
36.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2025282
56.3%
Lowercase Letter 1174718
32.6%
Dash Punctuation 400000
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 287551
14.2%
9 212346
10.5%
8 211864
10.5%
0 188172
9.3%
2 188148
9.3%
7 187813
9.3%
1 187606
9.3%
5 187480
9.3%
3 187225
9.2%
6 187077
9.2%
Lowercase Letter
ValueCountFrequency (%)
a 212644
18.1%
b 211946
18.0%
c 188078
16.0%
e 187476
16.0%
d 187374
16.0%
f 187200
15.9%
Dash Punctuation
ValueCountFrequency (%)
- 400000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2425282
67.4%
Latin 1174718
32.6%

Most frequent character per script

Common
ValueCountFrequency (%)
- 400000
16.5%
4 287551
11.9%
9 212346
8.8%
8 211864
8.7%
0 188172
7.8%
2 188148
7.8%
7 187813
7.7%
1 187606
7.7%
5 187480
7.7%
3 187225
7.7%
Latin
ValueCountFrequency (%)
a 212644
18.1%
b 211946
18.0%
c 188078
16.0%
e 187476
16.0%
d 187374
16.0%
f 187200
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3600000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 400000
 
11.1%
4 287551
 
8.0%
a 212644
 
5.9%
9 212346
 
5.9%
b 211946
 
5.9%
8 211864
 
5.9%
0 188172
 
5.2%
2 188148
 
5.2%
c 188078
 
5.2%
7 187813
 
5.2%
Other values (7) 1311438
36.4%

Customer ID
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct81999
Distinct (%)82.0%
Missing514
Missing (%)0.5%
Memory size785.4 KiB
9ef4f39f-b848-4227-803b-89fb1188f7d5
 
2
491764d1-3c6e-4c63-8b82-e03f75b36fd1
 
2
94a7d438-07d8-4ea7-9f79-4b4f88218a8b
 
2
02a9852c-ae76-486c-9397-11178993769f
 
2
02a6ae13-b430-4b80-83f3-cf087e2c61d9
 
2
Other values (81994)
99990 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters3600000
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63998 ?
Unique (%)64.0%

Sample

1st row981165ec-3274-42f5-a3b4-d104041a9ca9
2nd row2de017a3-2e01-49cb-a581-08169e83be29
3rd row5efb2b2b-bf11-4dfd-a572-3761a2694725
4th rowe777faab-98ae-45af-9a86-7ce5b33b1011
5th row81536ad9-5ccf-4eb8-befb-47a4d608658e

Common Values

ValueCountFrequency (%)
9ef4f39f-b848-4227-803b-89fb1188f7d5 2
 
< 0.1%
491764d1-3c6e-4c63-8b82-e03f75b36fd1 2
 
< 0.1%
94a7d438-07d8-4ea7-9f79-4b4f88218a8b 2
 
< 0.1%
02a9852c-ae76-486c-9397-11178993769f 2
 
< 0.1%
02a6ae13-b430-4b80-83f3-cf087e2c61d9 2
 
< 0.1%
cea9784f-9337-43fe-82c9-a952d95ed645 2
 
< 0.1%
2396973d-1e39-4ce8-b1a6-8b30139d1024 2
 
< 0.1%
81483803-71b4-406f-90f2-44c433434e93 2
 
< 0.1%
aa0a2649-6ccf-43da-92f2-2cfdf532bb66 2
 
< 0.1%
4bf54b6f-e8a2-4d0e-aee2-3b3d98c09a34 2
 
< 0.1%
Other values (81989) 99980
99.5%
(Missing) 514
 
0.5%

Length

2023-08-29T23:42:22.136227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9ef4f39f-b848-4227-803b-89fb1188f7d5 2
 
< 0.1%
56dd466b-e6d7-4c85-8c0c-68f852baa539 2
 
< 0.1%
cfff63a5-50de-438a-8565-6269a77bf751 2
 
< 0.1%
bc56e2c1-7abc-41ef-83b7-4d23e5f7712d 2
 
< 0.1%
0dbcc7e3-689d-4a60-b009-6e4ef2c9c4d9 2
 
< 0.1%
418617e7-ccee-4269-948d-067fbbdea1a0 2
 
< 0.1%
3006517f-2af0-439c-8400-718fa53ca1b6 2
 
< 0.1%
1c10b8f0-fc62-426a-a081-ac6b07c10901 2
 
< 0.1%
7e30498f-0593-4e71-99ed-5fd7d06ea8a2 2
 
< 0.1%
47c7ac5e-bb4b-4033-ad05-08bea24e69b7 2
 
< 0.1%
Other values (81989) 99980
> 99.9%

Most occurring characters

ValueCountFrequency (%)
- 400000
 
11.1%
4 287889
 
8.0%
a 212705
 
5.9%
8 212170
 
5.9%
b 211850
 
5.9%
9 211791
 
5.9%
c 188049
 
5.2%
e 188024
 
5.2%
7 187963
 
5.2%
5 187790
 
5.2%
Other values (7) 1311769
36.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2024943
56.2%
Lowercase Letter 1175057
32.6%
Dash Punctuation 400000
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 287889
14.2%
8 212170
10.5%
9 211791
10.5%
7 187963
9.3%
5 187790
9.3%
6 187624
9.3%
3 187601
9.3%
1 187434
9.3%
0 187400
9.3%
2 187281
9.2%
Lowercase Letter
ValueCountFrequency (%)
a 212705
18.1%
b 211850
18.0%
c 188049
16.0%
e 188024
16.0%
d 187287
15.9%
f 187142
15.9%
Dash Punctuation
ValueCountFrequency (%)
- 400000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2424943
67.4%
Latin 1175057
32.6%

Most frequent character per script

Common
ValueCountFrequency (%)
- 400000
16.5%
4 287889
11.9%
8 212170
8.7%
9 211791
8.7%
7 187963
7.8%
5 187790
7.7%
6 187624
7.7%
3 187601
7.7%
1 187434
7.7%
0 187400
7.7%
Latin
ValueCountFrequency (%)
a 212705
18.1%
b 211850
18.0%
c 188049
16.0%
e 188024
16.0%
d 187287
15.9%
f 187142
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3600000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 400000
 
11.1%
4 287889
 
8.0%
a 212705
 
5.9%
8 212170
 
5.9%
b 211850
 
5.9%
9 211791
 
5.9%
c 188049
 
5.2%
e 188024
 
5.2%
7 187963
 
5.2%
5 187790
 
5.2%
Other values (7) 1311769
36.4%

Loan Status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing514
Missing (%)0.5%
Memory size785.4 KiB
Fully Paid
77361 
Charged Off
22639 

Length

Max length11
Median length10
Mean length10.22639
Min length10

Characters and Unicode

Total characters1022639
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFully Paid
2nd rowFully Paid
3rd rowFully Paid
4th rowFully Paid
5th rowFully Paid

Common Values

ValueCountFrequency (%)
Fully Paid 77361
77.0%
Charged Off 22639
 
22.5%
(Missing) 514
 
0.5%

Length

2023-08-29T23:42:22.392794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-29T23:42:22.688850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
fully 77361
38.7%
paid 77361
38.7%
charged 22639
 
11.3%
off 22639
 
11.3%

Most occurring characters

ValueCountFrequency (%)
l 154722
15.1%
100000
9.8%
a 100000
9.8%
d 100000
9.8%
F 77361
7.6%
u 77361
7.6%
y 77361
7.6%
P 77361
7.6%
i 77361
7.6%
f 45278
 
4.4%
Other values (6) 135834
13.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 722639
70.7%
Uppercase Letter 200000
 
19.6%
Space Separator 100000
 
9.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 154722
21.4%
a 100000
13.8%
d 100000
13.8%
u 77361
10.7%
y 77361
10.7%
i 77361
10.7%
f 45278
 
6.3%
h 22639
 
3.1%
r 22639
 
3.1%
g 22639
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
F 77361
38.7%
P 77361
38.7%
C 22639
 
11.3%
O 22639
 
11.3%
Space Separator
ValueCountFrequency (%)
100000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 922639
90.2%
Common 100000
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 154722
16.8%
a 100000
10.8%
d 100000
10.8%
F 77361
8.4%
u 77361
8.4%
y 77361
8.4%
P 77361
8.4%
i 77361
8.4%
f 45278
 
4.9%
C 22639
 
2.5%
Other values (5) 113195
12.3%
Common
ValueCountFrequency (%)
100000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1022639
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 154722
15.1%
100000
9.8%
a 100000
9.8%
d 100000
9.8%
F 77361
7.6%
u 77361
7.6%
y 77361
7.6%
P 77361
7.6%
i 77361
7.6%
f 45278
 
4.4%
Other values (6) 135834
13.3%

Current Loan Amount
Real number (ℝ)

Distinct22004
Distinct (%)22.0%
Missing514
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean11760447
Minimum10802
Maximum99999999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size785.4 KiB
2023-08-29T23:42:22.948906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10802
5-th percentile76054
Q1179652
median312246
Q3524942
95-th percentile99999999
Maximum99999999
Range99989197
Interquartile range (IQR)345290

Descriptive statistics

Standard deviation31783943
Coefficient of variation (CV)2.7026134
Kurtosis3.8373662
Mean11760447
Median Absolute Deviation (MAD)147664
Skewness2.4159866
Sum1.1760447 × 1012
Variance1.010219 × 1015
MonotonicityNot monotonic
2023-08-29T23:42:23.266799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99999999 11484
 
11.4%
216194 27
 
< 0.1%
223652 27
 
< 0.1%
223102 27
 
< 0.1%
223322 27
 
< 0.1%
108966 26
 
< 0.1%
222596 26
 
< 0.1%
214962 25
 
< 0.1%
216810 25
 
< 0.1%
179828 25
 
< 0.1%
Other values (21994) 88281
87.8%
(Missing) 514
 
0.5%
ValueCountFrequency (%)
10802 1
 
< 0.1%
11242 1
 
< 0.1%
15422 2
 
< 0.1%
21098 1
 
< 0.1%
21450 3
 
< 0.1%
21472 9
< 0.1%
21494 3
 
< 0.1%
21516 6
< 0.1%
21538 7
< 0.1%
21560 4
< 0.1%
ValueCountFrequency (%)
99999999 11484
11.4%
789250 3
 
< 0.1%
789184 6
 
< 0.1%
789096 16
 
< 0.1%
789030 9
 
< 0.1%
788942 9
 
< 0.1%
788876 4
 
< 0.1%
788788 5
 
< 0.1%
788722 2
 
< 0.1%
788634 8
 
< 0.1%

Term
Categorical

Distinct2
Distinct (%)< 0.1%
Missing514
Missing (%)0.5%
Memory size785.4 KiB
Short Term
72208 
Long Term
27792 

Length

Max length10
Median length10
Mean length9.72208
Min length9

Characters and Unicode

Total characters972208
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowShort Term
2nd rowShort Term
3rd rowShort Term
4th rowLong Term
5th rowShort Term

Common Values

ValueCountFrequency (%)
Short Term 72208
71.8%
Long Term 27792
 
27.6%
(Missing) 514
 
0.5%

Length

2023-08-29T23:42:23.561705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-29T23:42:23.834428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
term 100000
50.0%
short 72208
36.1%
long 27792
 
13.9%

Most occurring characters

ValueCountFrequency (%)
r 172208
17.7%
o 100000
10.3%
100000
10.3%
T 100000
10.3%
e 100000
10.3%
m 100000
10.3%
S 72208
7.4%
h 72208
7.4%
t 72208
7.4%
L 27792
 
2.9%
Other values (2) 55584
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 672208
69.1%
Uppercase Letter 200000
 
20.6%
Space Separator 100000
 
10.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 172208
25.6%
o 100000
14.9%
e 100000
14.9%
m 100000
14.9%
h 72208
10.7%
t 72208
10.7%
n 27792
 
4.1%
g 27792
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
T 100000
50.0%
S 72208
36.1%
L 27792
 
13.9%
Space Separator
ValueCountFrequency (%)
100000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 872208
89.7%
Common 100000
 
10.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 172208
19.7%
o 100000
11.5%
T 100000
11.5%
e 100000
11.5%
m 100000
11.5%
S 72208
8.3%
h 72208
8.3%
t 72208
8.3%
L 27792
 
3.2%
n 27792
 
3.2%
Common
ValueCountFrequency (%)
100000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 972208
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 172208
17.7%
o 100000
10.3%
100000
10.3%
T 100000
10.3%
e 100000
10.3%
m 100000
10.3%
S 72208
7.4%
h 72208
7.4%
t 72208
7.4%
L 27792
 
2.9%
Other values (2) 55584
 
5.7%

Credit Score
Real number (ℝ)

Distinct324
Distinct (%)0.4%
Missing19668
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean1076.4561
Minimum585
Maximum7510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size785.4 KiB
2023-08-29T23:42:24.089094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum585
5-th percentile661
Q1705
median724
Q3741
95-th percentile6690
Maximum7510
Range6925
Interquartile range (IQR)36

Descriptive statistics

Standard deviation1475.4038
Coefficient of variation (CV)1.3706121
Kurtosis12.971832
Mean1076.4561
Median Absolute Deviation (MAD)17
Skewness3.8632253
Sum87027169
Variance2176816.3
MonotonicityNot monotonic
2023-08-29T23:42:24.377381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
747 1825
 
1.8%
740 1746
 
1.7%
746 1742
 
1.7%
741 1732
 
1.7%
742 1723
 
1.7%
739 1624
 
1.6%
745 1612
 
1.6%
748 1598
 
1.6%
743 1555
 
1.5%
725 1548
 
1.5%
Other values (314) 64141
63.8%
(Missing) 19668
 
19.6%
ValueCountFrequency (%)
585 12
< 0.1%
586 7
 
< 0.1%
587 11
< 0.1%
588 20
< 0.1%
589 6
 
< 0.1%
590 8
 
< 0.1%
591 9
< 0.1%
592 4
 
< 0.1%
593 7
 
< 0.1%
594 10
< 0.1%
ValueCountFrequency (%)
7510 9
 
< 0.1%
7500 24
 
< 0.1%
7490 23
 
< 0.1%
7480 43
< 0.1%
7470 51
0.1%
7460 76
0.1%
7450 55
0.1%
7440 58
0.1%
7430 70
0.1%
7420 84
0.1%

Annual Income
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct36174
Distinct (%)44.7%
Missing19668
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean1378276.6
Minimum76627
Maximum1.6555739 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size785.4 KiB
2023-08-29T23:42:24.753623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum76627
5-th percentile519863.75
Q1848844
median1174162
Q31650663
95-th percentile2810579.8
Maximum1.6555739 × 108
Range1.6548077 × 108
Interquartile range (IQR)801819

Descriptive statistics

Standard deviation1081360.2
Coefficient of variation (CV)0.78457418
Kurtosis6624.1677
Mean1378276.6
Median Absolute Deviation (MAD)380703
Skewness46.888699
Sum1.1142815 × 1011
Variance1.1693399 × 1012
MonotonicityNot monotonic
2023-08-29T23:42:25.064672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1162572 22
 
< 0.1%
973370 19
 
< 0.1%
969475 18
 
< 0.1%
1140000 18
 
< 0.1%
1143762 17
 
< 0.1%
949905 17
 
< 0.1%
1112640 17
 
< 0.1%
953230 17
 
< 0.1%
1320291 17
 
< 0.1%
1166220 17
 
< 0.1%
Other values (36164) 80667
80.3%
(Missing) 19668
 
19.6%
ValueCountFrequency (%)
76627 1
< 0.1%
81092 1
< 0.1%
94867 1
< 0.1%
97033 1
< 0.1%
106533 1
< 0.1%
111245 2
< 0.1%
130150 1
< 0.1%
134881 2
< 0.1%
135071 2
< 0.1%
144267 2
< 0.1%
ValueCountFrequency (%)
165557393 1
< 0.1%
36475440 1
< 0.1%
30838995 1
< 0.1%
28095300 1
< 0.1%
24161540 1
< 0.1%
23980375 1
< 0.1%
22448880 2
< 0.1%
19019000 1
< 0.1%
18768200 2
< 0.1%
18743937 1
< 0.1%
Distinct11
Distinct (%)< 0.1%
Missing4736
Missing (%)4.7%
Memory size785.4 KiB
10+ years
31121 
2 years
9134 
3 years
8169 
< 1 year
8164 
5 years
6787 
Other values (6)
32403 

Length

Max length9
Median length7
Mean length7.6676481
Min length6

Characters and Unicode

Total characters734392
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8 years
2nd row10+ years
3rd row8 years
4th row3 years
5th row5 years

Common Values

ValueCountFrequency (%)
10+ years 31121
31.0%
2 years 9134
 
9.1%
3 years 8169
 
8.1%
< 1 year 8164
 
8.1%
5 years 6787
 
6.8%
1 year 6460
 
6.4%
4 years 6143
 
6.1%
6 years 5686
 
5.7%
7 years 5577
 
5.5%
8 years 4582
 
4.6%
(Missing) 4736
 
4.7%

Length

2023-08-29T23:42:25.387668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years 81154
40.6%
10 31121
 
15.6%
1 14624
 
7.3%
year 14624
 
7.3%
2 9134
 
4.6%
3 8169
 
4.1%
8164
 
4.1%
5 6787
 
3.4%
4 6143
 
3.1%
6 5686
 
2.8%
Other values (3) 14114
 
7.1%

Most occurring characters

ValueCountFrequency (%)
103942
14.2%
y 95778
13.0%
e 95778
13.0%
a 95778
13.0%
r 95778
13.0%
s 81154
11.1%
1 45745
6.2%
0 31121
 
4.2%
+ 31121
 
4.2%
2 9134
 
1.2%
Other values (8) 49063
6.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 464266
63.2%
Decimal Number 126899
 
17.3%
Space Separator 103942
 
14.2%
Math Symbol 39285
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 45745
36.0%
0 31121
24.5%
2 9134
 
7.2%
3 8169
 
6.4%
5 6787
 
5.3%
4 6143
 
4.8%
6 5686
 
4.5%
7 5577
 
4.4%
8 4582
 
3.6%
9 3955
 
3.1%
Lowercase Letter
ValueCountFrequency (%)
y 95778
20.6%
e 95778
20.6%
a 95778
20.6%
r 95778
20.6%
s 81154
17.5%
Math Symbol
ValueCountFrequency (%)
+ 31121
79.2%
< 8164
 
20.8%
Space Separator
ValueCountFrequency (%)
103942
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 464266
63.2%
Common 270126
36.8%

Most frequent character per script

Common
ValueCountFrequency (%)
103942
38.5%
1 45745
16.9%
0 31121
 
11.5%
+ 31121
 
11.5%
2 9134
 
3.4%
3 8169
 
3.0%
< 8164
 
3.0%
5 6787
 
2.5%
4 6143
 
2.3%
6 5686
 
2.1%
Other values (3) 14114
 
5.2%
Latin
ValueCountFrequency (%)
y 95778
20.6%
e 95778
20.6%
a 95778
20.6%
r 95778
20.6%
s 81154
17.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 734392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
103942
14.2%
y 95778
13.0%
e 95778
13.0%
a 95778
13.0%
r 95778
13.0%
s 81154
11.1%
1 45745
6.2%
0 31121
 
4.2%
+ 31121
 
4.2%
2 9134
 
1.2%
Other values (8) 49063
6.7%

Home Ownership
Categorical

Distinct4
Distinct (%)< 0.1%
Missing514
Missing (%)0.5%
Memory size785.4 KiB
Home Mortgage
48410 
Rent
42194 
Own Home
9182 
HaveMortgage
 
214

Length

Max length13
Median length12
Mean length8.7413
Min length4

Characters and Unicode

Total characters874130
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHome Mortgage
2nd rowHome Mortgage
3rd rowOwn Home
4th rowOwn Home
5th rowRent

Common Values

ValueCountFrequency (%)
Home Mortgage 48410
48.2%
Rent 42194
42.0%
Own Home 9182
 
9.1%
HaveMortgage 214
 
0.2%
(Missing) 514
 
0.5%

Length

2023-08-29T23:42:25.764320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-29T23:42:26.249086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
home 57592
36.5%
mortgage 48410
30.7%
rent 42194
26.8%
own 9182
 
5.8%
havemortgage 214
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 148624
17.0%
o 106216
12.2%
g 97248
11.1%
t 90818
10.4%
H 57806
 
6.6%
m 57592
 
6.6%
57592
 
6.6%
n 51376
 
5.9%
a 48838
 
5.6%
M 48624
 
5.6%
Other values (5) 109396
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 658732
75.4%
Uppercase Letter 157806
 
18.1%
Space Separator 57592
 
6.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 148624
22.6%
o 106216
16.1%
g 97248
14.8%
t 90818
13.8%
m 57592
 
8.7%
n 51376
 
7.8%
a 48838
 
7.4%
r 48624
 
7.4%
w 9182
 
1.4%
v 214
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
H 57806
36.6%
M 48624
30.8%
R 42194
26.7%
O 9182
 
5.8%
Space Separator
ValueCountFrequency (%)
57592
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 816538
93.4%
Common 57592
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 148624
18.2%
o 106216
13.0%
g 97248
11.9%
t 90818
11.1%
H 57806
 
7.1%
m 57592
 
7.1%
n 51376
 
6.3%
a 48838
 
6.0%
M 48624
 
6.0%
r 48624
 
6.0%
Other values (4) 60772
7.4%
Common
ValueCountFrequency (%)
57592
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 874130
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 148624
17.0%
o 106216
12.2%
g 97248
11.1%
t 90818
10.4%
H 57806
 
6.6%
m 57592
 
6.6%
57592
 
6.6%
n 51376
 
5.9%
a 48838
 
5.6%
M 48624
 
5.6%
Other values (5) 109396
12.5%

Purpose
Categorical

Distinct16
Distinct (%)< 0.1%
Missing514
Missing (%)0.5%
Memory size785.4 KiB
Debt Consolidation
78552 
other
 
6037
Home Improvements
 
5839
Other
 
3250
Business Loan
 
1569
Other values (11)
 
4753

Length

Max length20
Median length18
Mean length16.32015
Min length5

Characters and Unicode

Total characters1632015
Distinct characters35
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHome Improvements
2nd rowDebt Consolidation
3rd rowDebt Consolidation
4th rowDebt Consolidation
5th rowDebt Consolidation

Common Values

ValueCountFrequency (%)
Debt Consolidation 78552
78.2%
other 6037
 
6.0%
Home Improvements 5839
 
5.8%
Other 3250
 
3.2%
Business Loan 1569
 
1.6%
Buy a Car 1265
 
1.3%
Medical Bills 1127
 
1.1%
Buy House 678
 
0.7%
Take a Trip 573
 
0.6%
major_purchase 352
 
0.4%
Other values (6) 758
 
0.8%
(Missing) 514
 
0.5%

Length

2023-08-29T23:42:26.759563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debt 78552
41.0%
consolidation 78552
41.0%
other 9287
 
4.8%
home 5839
 
3.0%
improvements 5839
 
3.0%
buy 1943
 
1.0%
a 1838
 
1.0%
business 1569
 
0.8%
loan 1569
 
0.8%
car 1265
 
0.7%
Other values (13) 5287
 
2.8%

Most occurring characters

ValueCountFrequency (%)
o 256320
15.7%
t 172430
10.6%
n 166948
10.2%
i 162248
9.9%
e 110301
 
6.8%
s 92585
 
5.7%
91540
 
5.6%
a 86321
 
5.3%
l 82608
 
5.1%
d 80008
 
4.9%
Other values (25) 330706
20.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1357176
83.2%
Uppercase Letter 182654
 
11.2%
Space Separator 91540
 
5.6%
Connector Punctuation 645
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 256320
18.9%
t 172430
12.7%
n 166948
12.3%
i 162248
12.0%
e 110301
8.1%
s 92585
 
6.8%
a 86321
 
6.4%
l 82608
 
6.1%
d 80008
 
5.9%
b 78845
 
5.8%
Other values (13) 68562
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
C 79817
43.7%
D 78552
43.0%
H 6517
 
3.6%
I 5839
 
3.2%
B 4639
 
2.5%
O 3250
 
1.8%
L 1569
 
0.9%
T 1146
 
0.6%
M 1127
 
0.6%
E 198
 
0.1%
Space Separator
ValueCountFrequency (%)
91540
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 645
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1539830
94.4%
Common 92185
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 256320
16.6%
t 172430
11.2%
n 166948
10.8%
i 162248
10.5%
e 110301
7.2%
s 92585
 
6.0%
a 86321
 
5.6%
l 82608
 
5.4%
d 80008
 
5.2%
C 79817
 
5.2%
Other values (23) 250244
16.3%
Common
ValueCountFrequency (%)
91540
99.3%
_ 645
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1632015
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 256320
15.7%
t 172430
10.6%
n 166948
10.2%
i 162248
9.9%
e 110301
 
6.8%
s 92585
 
5.7%
91540
 
5.6%
a 86321
 
5.3%
l 82608
 
5.1%
d 80008
 
4.9%
Other values (25) 330706
20.3%

Monthly Debt
Real number (ℝ)

Distinct65765
Distinct (%)65.8%
Missing514
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean18472.412
Minimum0
Maximum435843.28
Zeros74
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size785.4 KiB
2023-08-29T23:42:27.341567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3714.823
Q110214.162
median16220.3
Q324012.057
95-th percentile40477.609
Maximum435843.28
Range435843.28
Interquartile range (IQR)13797.895

Descriptive statistics

Standard deviation12174.993
Coefficient of variation (CV)0.65909056
Kurtosis22.193058
Mean18472.412
Median Absolute Deviation (MAD)6724.48
Skewness2.2139416
Sum1.8472412 × 109
Variance1.4823045 × 108
MonotonicityNot monotonic
2023-08-29T23:42:27.878876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 74
 
0.1%
11162.88 9
 
< 0.1%
15903 9
 
< 0.1%
13033.43 8
 
< 0.1%
12967.5 8
 
< 0.1%
16279.2 8
 
< 0.1%
10647.98 8
 
< 0.1%
13359.85 8
 
< 0.1%
14726.52 8
 
< 0.1%
12656.47 8
 
< 0.1%
Other values (65755) 99852
99.3%
(Missing) 514
 
0.5%
ValueCountFrequency (%)
0 74
0.1%
7.41 2
 
< 0.1%
12.92 1
 
< 0.1%
17.1 1
 
< 0.1%
19.57 1
 
< 0.1%
20.71 1
 
< 0.1%
22.23 1
 
< 0.1%
28.5 1
 
< 0.1%
34.96 2
 
< 0.1%
41.99 1
 
< 0.1%
ValueCountFrequency (%)
435843.28 1
< 0.1%
229057.92 1
< 0.1%
205801.35 1
< 0.1%
173265.56 2
< 0.1%
172156.15 1
< 0.1%
165810.53 1
< 0.1%
165437.18 2
< 0.1%
152512.24 1
< 0.1%
152331.93 1
< 0.1%
147152.53 1
< 0.1%

Years of Credit History
Real number (ℝ)

Distinct506
Distinct (%)0.5%
Missing514
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean18.199141
Minimum3.6
Maximum70.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size785.4 KiB
2023-08-29T23:42:28.246619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.6
5-th percentile9
Q113.5
median16.9
Q321.7
95-th percentile31.7
Maximum70.5
Range66.9
Interquartile range (IQR)8.2

Descriptive statistics

Standard deviation7.0153236
Coefficient of variation (CV)0.38547554
Kurtosis1.7407019
Mean18.199141
Median Absolute Deviation (MAD)4
Skewness1.0715509
Sum1819914.1
Variance49.214766
MonotonicityNot monotonic
2023-08-29T23:42:29.161857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 1340
 
1.3%
15 1305
 
1.3%
17 1219
 
1.2%
16.5 1176
 
1.2%
14 1151
 
1.1%
15.4 1065
 
1.1%
17.5 1025
 
1.0%
13 1021
 
1.0%
14.5 980
 
1.0%
18 967
 
1.0%
Other values (496) 88751
88.3%
ValueCountFrequency (%)
3.6 1
 
< 0.1%
3.7 2
 
< 0.1%
3.8 3
 
< 0.1%
3.9 4
 
< 0.1%
4 6
 
< 0.1%
4.1 8
 
< 0.1%
4.2 20
< 0.1%
4.3 9
< 0.1%
4.4 18
< 0.1%
4.5 17
< 0.1%
ValueCountFrequency (%)
70.5 1
< 0.1%
65 2
< 0.1%
60.5 2
< 0.1%
59.9 1
< 0.1%
59.7 1
< 0.1%
59.5 2
< 0.1%
58 1
< 0.1%
57.7 2
< 0.1%
57.5 1
< 0.1%
57 1
< 0.1%
Distinct116
Distinct (%)0.2%
Missing53655
Missing (%)53.4%
Infinite0
Infinite (%)0.0%
Mean34.901321
Minimum0
Maximum176
Zeros216
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size785.4 KiB
2023-08-29T23:42:29.722019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q116
median32
Q351
95-th percentile75
Maximum176
Range176
Interquartile range (IQR)35

Descriptive statistics

Standard deviation21.997829
Coefficient of variation (CV)0.63028642
Kurtosis-0.74578961
Mean34.901321
Median Absolute Deviation (MAD)17
Skewness0.43436156
Sum1635441
Variance483.90447
MonotonicityNot monotonic
2023-08-29T23:42:30.160009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 922
 
0.9%
12 902
 
0.9%
14 877
 
0.9%
15 865
 
0.9%
10 861
 
0.9%
8 856
 
0.9%
9 849
 
0.8%
18 847
 
0.8%
16 837
 
0.8%
6 836
 
0.8%
Other values (106) 38207
38.0%
(Missing) 53655
53.4%
ValueCountFrequency (%)
0 216
 
0.2%
1 289
 
0.3%
2 418
0.4%
3 445
0.4%
4 513
0.5%
5 703
0.7%
6 836
0.8%
7 825
0.8%
8 856
0.9%
9 849
0.8%
ValueCountFrequency (%)
176 2
< 0.1%
152 1
< 0.1%
148 1
< 0.1%
143 1
< 0.1%
141 1
< 0.1%
139 1
< 0.1%
131 1
< 0.1%
130 1
< 0.1%
129 1
< 0.1%
120 2
< 0.1%

Number of Open Accounts
Real number (ℝ)

Distinct51
Distinct (%)0.1%
Missing514
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean11.12853
Minimum0
Maximum76
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size785.4 KiB
2023-08-29T23:42:30.507770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q18
median10
Q314
95-th percentile20
Maximum76
Range76
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.0098704
Coefficient of variation (CV)0.45018258
Kurtosis3.0427669
Mean11.12853
Median Absolute Deviation (MAD)3
Skewness1.1792013
Sum1112853
Variance25.098801
MonotonicityNot monotonic
2023-08-29T23:42:30.851833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 9360
 
9.3%
10 9012
 
9.0%
8 8792
 
8.7%
11 8601
 
8.6%
7 8090
 
8.0%
12 7461
 
7.4%
6 6731
 
6.7%
13 6280
 
6.2%
14 5194
 
5.2%
5 4742
 
4.7%
Other values (41) 25737
25.6%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 25
 
< 0.1%
2 448
 
0.4%
3 1364
 
1.4%
4 2849
 
2.8%
5 4742
4.7%
6 6731
6.7%
7 8090
8.0%
8 8792
8.7%
9 9360
9.3%
ValueCountFrequency (%)
76 2
 
< 0.1%
56 2
 
< 0.1%
52 2
 
< 0.1%
48 4
 
< 0.1%
47 3
 
< 0.1%
45 6
< 0.1%
44 5
< 0.1%
43 10
< 0.1%
42 5
< 0.1%
41 7
< 0.1%

Number of Credit Problems
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)< 0.1%
Missing514
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.16831
Minimum0
Maximum15
Zeros86035
Zeros (%)85.6%
Negative0
Negative (%)0.0%
Memory size785.4 KiB
2023-08-29T23:42:31.207476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.48270496
Coefficient of variation (CV)2.8679517
Kurtosis48.012465
Mean0.16831
Median Absolute Deviation (MAD)0
Skewness4.8231356
Sum16831
Variance0.23300407
MonotonicityNot monotonic
2023-08-29T23:42:31.467623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 86035
85.6%
1 12077
 
12.0%
2 1299
 
1.3%
3 378
 
0.4%
4 125
 
0.1%
5 49
 
< 0.1%
6 17
 
< 0.1%
7 8
 
< 0.1%
8 4
 
< 0.1%
11 2
 
< 0.1%
Other values (4) 6
 
< 0.1%
(Missing) 514
 
0.5%
ValueCountFrequency (%)
0 86035
85.6%
1 12077
 
12.0%
2 1299
 
1.3%
3 378
 
0.4%
4 125
 
0.1%
5 49
 
< 0.1%
6 17
 
< 0.1%
7 8
 
< 0.1%
8 4
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
12 1
 
< 0.1%
11 2
 
< 0.1%
10 2
 
< 0.1%
9 2
 
< 0.1%
8 4
 
< 0.1%
7 8
 
< 0.1%
6 17
 
< 0.1%
5 49
 
< 0.1%
4 125
0.1%

Current Credit Balance
Real number (ℝ)

Distinct32730
Distinct (%)32.7%
Missing514
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean294637.38
Minimum0
Maximum32878968
Zeros572
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size785.4 KiB
2023-08-29T23:42:31.792884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30400
Q1112670
median209817
Q3367958.75
95-th percentile760741
Maximum32878968
Range32878968
Interquartile range (IQR)255288.75

Descriptive statistics

Standard deviation376170.93
Coefficient of variation (CV)1.2767251
Kurtosis697.49819
Mean294637.38
Median Absolute Deviation (MAD)115007
Skewness14.154428
Sum2.9463738 × 1010
Variance1.4150457 × 1011
MonotonicityNot monotonic
2023-08-29T23:42:32.106446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 572
 
0.6%
67697 17
 
< 0.1%
65683 17
 
< 0.1%
175978 17
 
< 0.1%
124013 16
 
< 0.1%
246373 16
 
< 0.1%
88426 16
 
< 0.1%
100301 16
 
< 0.1%
148846 15
 
< 0.1%
118009 15
 
< 0.1%
Other values (32720) 99283
98.8%
(Missing) 514
 
0.5%
ValueCountFrequency (%)
0 572
0.6%
19 12
 
< 0.1%
38 9
 
< 0.1%
57 7
 
< 0.1%
76 4
 
< 0.1%
95 6
 
< 0.1%
114 7
 
< 0.1%
133 5
 
< 0.1%
152 2
 
< 0.1%
171 4
 
< 0.1%
ValueCountFrequency (%)
32878968 1
< 0.1%
12986956 2
< 0.1%
12746397 2
< 0.1%
11796435 1
< 0.1%
11361924 1
< 0.1%
9134592 2
< 0.1%
7888952 1
< 0.1%
7749587 1
< 0.1%
7679344 1
< 0.1%
7666747 1
< 0.1%

Maximum Open Credit
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct44596
Distinct (%)44.6%
Missing516
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean760798.38
Minimum0
Maximum1.5397379 × 109
Zeros681
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size785.4 KiB
2023-08-29T23:42:32.441123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile109758
Q1273438
median467874
Q3782958
95-th percentile1640226.5
Maximum1.5397379 × 109
Range1.5397379 × 109
Interquartile range (IQR)509520

Descriptive statistics

Standard deviation8384503.5
Coefficient of variation (CV)11.020664
Kurtosis20394.84
Mean760798.38
Median Absolute Deviation (MAD)230901
Skewness132.63887
Sum7.6078317 × 1010
Variance7.0299898 × 1013
MonotonicityNot monotonic
2023-08-29T23:42:32.788083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 681
 
0.7%
237204 13
 
< 0.1%
155474 12
 
< 0.1%
150194 12
 
< 0.1%
242968 12
 
< 0.1%
201652 12
 
< 0.1%
236412 12
 
< 0.1%
107404 12
 
< 0.1%
246136 12
 
< 0.1%
181456 11
 
< 0.1%
Other values (44586) 99209
98.7%
(Missing) 516
 
0.5%
ValueCountFrequency (%)
0 681
0.7%
4334 4
 
< 0.1%
4444 1
 
< 0.1%
5390 1
 
< 0.1%
6446 5
 
< 0.1%
6468 3
 
< 0.1%
6490 2
 
< 0.1%
6512 2
 
< 0.1%
6534 1
 
< 0.1%
6556 4
 
< 0.1%
ValueCountFrequency (%)
1539737892 1
< 0.1%
1304726170 1
< 0.1%
980305260 1
< 0.1%
798255370 1
< 0.1%
632477736 1
< 0.1%
489343206 1
< 0.1%
380052288 1
< 0.1%
267490058 1
< 0.1%
265512874 1
< 0.1%
192284158 2
< 0.1%

Bankruptcies
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing718
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean0.11774019
Minimum0
Maximum7
Zeros88774
Zeros (%)88.3%
Negative0
Negative (%)0.0%
Memory size785.4 KiB
2023-08-29T23:42:33.083467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.35142382
Coefficient of variation (CV)2.9847397
Kurtosis18.527679
Mean0.11774019
Median Absolute Deviation (MAD)0
Skewness3.5058037
Sum11750
Variance0.1234987
MonotonicityNot monotonic
2023-08-29T23:42:33.340131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 88774
88.3%
1 10475
 
10.4%
2 417
 
0.4%
3 93
 
0.1%
4 27
 
< 0.1%
5 7
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
(Missing) 718
 
0.7%
ValueCountFrequency (%)
0 88774
88.3%
1 10475
 
10.4%
2 417
 
0.4%
3 93
 
0.1%
4 27
 
< 0.1%
5 7
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 2
 
< 0.1%
5 7
 
< 0.1%
4 27
 
< 0.1%
3 93
 
0.1%
2 417
 
0.4%
1 10475
 
10.4%
0 88774
88.3%

Tax Liens
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing524
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.029312931
Minimum0
Maximum15
Zeros98062
Zeros (%)97.6%
Negative0
Negative (%)0.0%
Memory size785.4 KiB
2023-08-29T23:42:33.591944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.25818244
Coefficient of variation (CV)8.8078
Kurtosis402.06666
Mean0.029312931
Median Absolute Deviation (MAD)0
Skewness15.50022
Sum2931
Variance0.06665817
MonotonicityNot monotonic
2023-08-29T23:42:33.833017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 98062
97.6%
1 1343
 
1.3%
2 374
 
0.4%
3 111
 
0.1%
4 58
 
0.1%
5 16
 
< 0.1%
6 12
 
< 0.1%
7 7
 
< 0.1%
9 3
 
< 0.1%
11 2
 
< 0.1%
Other values (2) 2
 
< 0.1%
(Missing) 524
 
0.5%
ValueCountFrequency (%)
0 98062
97.6%
1 1343
 
1.3%
2 374
 
0.4%
3 111
 
0.1%
4 58
 
0.1%
5 16
 
< 0.1%
6 12
 
< 0.1%
7 7
 
< 0.1%
9 3
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
11 2
 
< 0.1%
10 1
 
< 0.1%
9 3
 
< 0.1%
7 7
 
< 0.1%
6 12
 
< 0.1%
5 16
 
< 0.1%
4 58
 
0.1%
3 111
 
0.1%
2 374
0.4%

Interactions

2023-08-29T23:42:13.758056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:29.445129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:34.135623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:37.499829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:40.981354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:45.432513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:49.516632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:52.951651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:56.416910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:02.370025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:05.854096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:09.437875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:14.225181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:29.858340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:34.401750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:37.776465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:41.266724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:45.840568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:49.789830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:53.230077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:56.718440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:02.649195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:06.159998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:09.722149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:14.625195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:30.298487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:34.679778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:38.082423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:41.542734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:46.261338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:50.065404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:53.513296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:57.846283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:02.943671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:06.449093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:09.989465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:15.065820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:30.781186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:34.960743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:38.351427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:41.845813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:46.725074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:50.341422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:53.807614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:58.296908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:03.242815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:06.750536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:10.283117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:15.465315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:31.169488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:35.223672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:38.625257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:42.122830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:47.121921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:50.610117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:54.074552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:59.105054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:03.510250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:07.037283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:10.556948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:15.769794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:31.573478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:35.490411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:38.917889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:42.392347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:47.420171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:50.905467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:54.348113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:59.749171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:03.788311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:07.316411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:10.828006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:16.548918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:32.039300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:35.787344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:39.190502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:42.667569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:47.734943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:51.182827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:54.634712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:00.280880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:04.098233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:07.605270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:11.130702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:16.831137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:32.543713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:36.070013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:39.469706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:43.376194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:48.017627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:51.460823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:54.915985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:00.700883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:04.390303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:07.911770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:11.512417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:17.143086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:33.032622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:36.347837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:39.757958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:43.723273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:48.338542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:51.754328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:55.208418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:01.160796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:04.677482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:08.237333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:11.943074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:17.440911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:33.308194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:36.624195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:40.047002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:44.186263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:48.643047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:52.048071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:55.498279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:01.462394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:04.972975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:08.548309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:12.358897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:17.761199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:33.584863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:36.928063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:40.358514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:44.649155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:48.936047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:52.351643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:55.827627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:01.767382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:05.273276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:08.841877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:12.783717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:18.038627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:33.866158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:37.215614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:40.659350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:45.032123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:49.227033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:52.649440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:41:56.124159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:02.078080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:05.557639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:09.134476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-29T23:42:13.257564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-08-29T23:42:34.116530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Current Loan AmountCredit ScoreAnnual IncomeMonthly DebtYears of Credit HistoryMonths since last delinquentNumber of Open AccountsNumber of Credit ProblemsCurrent Credit BalanceMaximum Open CreditBankruptciesTax LiensLoan StatusTermYears in current jobHome OwnershipPurpose
Current Loan Amount1.000-0.0380.3820.3370.145-0.0230.176-0.0730.3680.358-0.0790.0130.1950.0610.0000.0090.020
Credit Score-0.0381.0000.014-0.0770.0820.040-0.010-0.067-0.0070.143-0.051-0.0280.4700.1070.0090.0130.022
Annual Income0.3820.0141.0000.5940.261-0.0870.242-0.0410.3910.392-0.0590.0410.0040.0000.0000.0040.000
Monthly Debt0.337-0.0770.5941.0000.218-0.0580.461-0.0660.5260.430-0.0770.0210.0080.0500.0140.0550.000
Years of Credit History0.1450.0820.2610.2181.000-0.0510.1460.0800.2670.2860.0800.0200.0340.0660.0920.1290.023
Months since last delinquent-0.0230.040-0.087-0.058-0.0511.000-0.0400.110-0.004-0.0270.1170.0120.0230.0170.0210.0400.008
Number of Open Accounts0.176-0.0100.2420.4610.146-0.0401.000-0.0080.3740.490-0.0180.0130.0130.0800.0250.0870.048
Number of Credit Problems-0.073-0.067-0.041-0.0660.0800.110-0.0081.000-0.206-0.1730.8680.3680.0000.0140.0070.0100.013
Current Credit Balance0.368-0.0070.3910.5260.267-0.0040.374-0.2061.0000.778-0.199-0.0320.0120.0120.0040.0200.000
Maximum Open Credit0.3580.1430.3920.4300.286-0.0270.490-0.1730.7781.000-0.169-0.0230.0000.0050.0040.0400.013
Bankruptcies-0.079-0.051-0.059-0.0770.0800.117-0.0180.868-0.199-0.1691.0000.0510.0000.0290.0200.0070.024
Tax Liens0.013-0.0280.0410.0210.0200.0120.0130.368-0.032-0.0230.0511.0000.0070.0000.0000.0100.010
Loan Status0.1950.4700.0040.0080.0340.0230.0130.0000.0120.0000.0000.0071.0000.1110.0160.0530.046
Term0.0610.1070.0000.0500.0660.0170.0800.0140.0120.0050.0290.0000.1111.0000.0700.1200.074
Years in current job0.0000.0090.0000.0140.0920.0210.0250.0070.0040.0040.0200.0000.0160.0701.0000.1230.029
Home Ownership0.0090.0130.0040.0550.1290.0400.0870.0100.0200.0400.0070.0100.0530.1200.1231.0000.370
Purpose0.0200.0220.0000.0000.0230.0080.0480.0130.0000.0130.0240.0100.0460.0740.0290.3701.000

Missing values

2023-08-29T23:42:18.572647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-29T23:42:19.483783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-29T23:42:20.834180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Loan IDCustomer IDLoan StatusCurrent Loan AmountTermCredit ScoreAnnual IncomeYears in current jobHome OwnershipPurposeMonthly DebtYears of Credit HistoryMonths since last delinquentNumber of Open AccountsNumber of Credit ProblemsCurrent Credit BalanceMaximum Open CreditBankruptciesTax Liens
014dd8831-6af5-400b-83ec-68e61888a048981165ec-3274-42f5-a3b4-d104041a9ca9Fully Paid445412.0Short Term709.01167493.08 yearsHome MortgageHome Improvements5214.7417.2NaN6.01.0228190.0416746.01.00.0
14771cc26-131a-45db-b5aa-537ea4ba53422de017a3-2e01-49cb-a581-08169e83be29Fully Paid262328.0Short TermNaNNaN10+ yearsHome MortgageDebt Consolidation33295.9821.18.035.00.0229976.0850784.00.00.0
24eed4e6a-aa2f-4c91-8651-ce984ee8fb265efb2b2b-bf11-4dfd-a572-3761a2694725Fully Paid99999999.0Short Term741.02231892.08 yearsOwn HomeDebt Consolidation29200.5314.929.018.01.0297996.0750090.00.00.0
377598f7b-32e7-4e3b-a6e5-06ba0d98fe8ae777faab-98ae-45af-9a86-7ce5b33b1011Fully Paid347666.0Long Term721.0806949.03 yearsOwn HomeDebt Consolidation8741.9012.0NaN9.00.0256329.0386958.00.00.0
4d4062e70-befa-4995-8643-a0de7393818281536ad9-5ccf-4eb8-befb-47a4d608658eFully Paid176220.0Short TermNaNNaN5 yearsRentDebt Consolidation20639.706.1NaN15.00.0253460.0427174.00.00.0
589d8cb0c-e5c2-4f54-b056-48a645c543dd4ffe99d3-7f2a-44db-afc1-40943f1f9750Charged Off206602.0Short Term7290.0896857.010+ yearsHome MortgageDebt Consolidation16367.7417.3NaN6.00.0215308.0272448.00.00.0
6273581de-85d8-4332-81a5-19b04ce6866690a75dde-34d5-419c-90dc-1e58b04b3e35Fully Paid217646.0Short Term730.01184194.0< 1 yearHome MortgageDebt Consolidation10855.0819.610.013.01.0122170.0272052.01.00.0
7db0dc6e1-77ee-4826-acca-772f9039e1c7018973c9-e316-4956-b363-67e134fb0931Charged Off648714.0Long TermNaNNaN< 1 yearHome MortgageBuy House14806.138.28.015.00.0193306.0864204.00.00.0
88af915d9-9e91-44a0-b5a2-564a45c12089af534dea-d27e-4fd6-9de8-efaa52a78ec0Fully Paid548746.0Short Term678.02559110.02 yearsRentDebt Consolidation18660.2822.633.04.00.0437171.0555038.00.00.0
90b1c4e3d-bd97-45ce-9622-22732fcdc9a0235c4a43-dadf-483d-aa44-9d6d77ae4583Fully Paid215952.0Short Term739.01454735.0< 1 yearRentDebt Consolidation39277.7513.9NaN20.00.0669560.01021460.00.00.0
Loan IDCustomer IDLoan StatusCurrent Loan AmountTermCredit ScoreAnnual IncomeYears in current jobHome OwnershipPurposeMonthly DebtYears of Credit HistoryMonths since last delinquentNumber of Open AccountsNumber of Credit ProblemsCurrent Credit BalanceMaximum Open CreditBankruptciesTax Liens
100504NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
100505NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
100506NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
100507NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
100508NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
100509NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
100510NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
100511NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
100512NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
100513NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Duplicate rows

Most frequently occurring

Loan IDCustomer IDLoan StatusCurrent Loan AmountTermCredit ScoreAnnual IncomeYears in current jobHome OwnershipPurposeMonthly DebtYears of Credit HistoryMonths since last delinquentNumber of Open AccountsNumber of Credit ProblemsCurrent Credit BalanceMaximum Open CreditBankruptciesTax Liens# duplicates
10215NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN514
0000bc65a-6a7c-4566-86f3-203b4ec35eca724bddb4-a23c-4759-ba6f-dc79c7dd5334Fully Paid642202.0Short Term715.01759533.02 yearsRentDebt Consolidation23020.5913.8NaN11.00.0445987.0733546.00.00.02
1000c16df-c24f-41cf-a90e-60301d131bb9b07c4262-70bb-41cc-b28a-d87540577fb1Fully Paid155496.0Short Term706.0664753.0NaNOwn Homeother8087.9221.3NaN7.01.079382.0150700.01.00.02
20016d326-7878-46bb-9c18-a75af255d7feeccd2965-56cf-4be0-99b2-893f8a520feaFully Paid88770.0Short Term700.01671221.02 yearsHome MortgageHome Improvements2562.5310.3NaN3.01.067279.0124234.01.00.02
30018f629-8cef-48bd-bb93-40179f24256c1e96933c-3a01-46b2-975d-06c5a2b469c3Fully Paid66396.0Short Term711.0535192.03 yearsRentDebt Consolidation9142.8015.850.08.00.0112347.0307538.00.00.02
4001a84a9-3fd5-4e82-9153-49325b996408b282e6f9-2d09-4988-b579-6d90d104e70dFully Paid180246.0Long Term658.0858097.02 yearsRentOther10289.8318.458.08.00.0288553.0440220.00.00.02
5001f3ce7-5277-4202-8511-27b464ea640496875077-865f-4998-9e93-59b016165fdfFully Paid65406.0Short Term705.01883090.03 yearsHome MortgageHome Improvements56021.8823.4NaN11.00.0408291.0527648.00.00.02
6002f3769-888e-4b66-b940-21ec4b7c2c69f49f2a18-deba-45de-8af6-e0174b9b4894Fully Paid142824.0Short Term724.0683183.04 yearsHome MortgageDebt Consolidation11272.5115.4NaN7.00.027322.077000.00.00.02
7003665f5-adff-4fbb-903c-14d022fa6a0880c0ee25-ec87-4e1f-aefc-e5bbd679cef1Fully Paid43692.0Short Term719.0679079.09 yearsHaveMortgageTake a Trip8601.6811.069.05.05.016264.0174328.01.04.02
8003d60c3-3a4c-4b81-b0f4-3105c34ce2e89d1e355e-f80d-40a8-b582-d391baa0dce9Fully Paid194370.0Short Term714.0991952.010+ yearsHome Mortgageother11242.1121.032.06.00.0168017.0448272.00.00.02